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Rubén Solano
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Advanced analytics and predictive models

Advanced analytics and predictive models for businesses

Forecasts and classifications built from your historical data, evaluated against simple alternatives and explained so you can decide with them, knowing what they can and cannot tell you.

Tell me what you want to predict or understandSee how I work

Updated:

Problems it solves

  • Deciding stock or capacity by eye

    Without a reasoned forecast you buy too much or too little, or size the team badly.

  • Not knowing which marketing channel works

    Each platform attributes sales to itself. A joint view of the effect of spend is missing.

  • Lots of data, few conclusions

    Reports describe the past, but nobody has quantified which factors explain the results.

  • Models nobody uses

    A model with a good metric in a notebook is useless if nobody builds it into a decision or maintains it.

How I handle uncertainty

A prediction without an error margin is an opinion. In every project I report, next to the expected value, a reasonable range and the model’s performance against a simple baseline (for example, repeating last week’s value). If the model does not consistently beat that baseline in a validation that respects time order, I will tell you and we will not put it into production.

Evaluation: what makes a model useful or useless

I use temporal validation (train on the past, test on the future) to avoid information leakage, which artificially inflates metrics. I choose metrics according to the cost of being wrong: under-predicting is not the same as over-predicting. I also analyse where the model fails — certain products, months or customers — because that is usually where the most useful improvements are.

From analysis to use

A model is useful when someone acts on it. That is why from the start I settle how results will be consumed and how often they are refreshed. Once the model is in use, I monitor the gap between forecast and outcome and retrain at an agreed frequency.

Under projects you can see a river-level forecast using time series and lagged variables, and a marketing mix modelling project using Bayesian inference — both personal, with their methodology and limitations described.

What you get

  • Exploratory analysis and data diagnosis

    What data exists, its quality, the patterns it shows and what can and cannot be concluded from it.

  • Predictive or statistical model

    Forecast, classification or response model, always compared with a simple baseline (such as repeating the previous value).

  • Honest evaluation

    Temporal or cross validation, business-relevant metrics, uncertainty intervals and error analysis.

  • Putting it to use

    A periodic report, results table or connection to your BI tool, with retraining and quality monitoring.

  • Documentation of assumptions and limits

    What the model assumes, when it stops being reliable and how to interpret its results.

How I approach it

  1. 01

    Translate the business question

    Which decision will be made with the result, over what horizon and what error is acceptable.

  2. 02

    Review and prepare the data

    Historical coverage, gaps, definition changes and the variables available at the time of prediction.

  3. 03

    Baseline and candidate models

    I start with simple methods; I only add complexity if it improves consistently in validation.

  4. 04

    Validation without information leakage

    Temporal split between training and test, so the evaluation resembles real use.

  5. 05

    Delivery, monitoring and retraining

    Measure in production whether the model keeps working and retrain when the data changes.

Concrete examples

  • Demand forecasting

    Estimate sales or orders by product or period with seasonality, trend and variables such as promotions or calendar.

  • Classification and prioritisation

    Rank leads, tickets or customers by the probability of an outcome to decide where to act first.

  • Business performance analysis

    Quantify which factors explain changes in revenue, margin or conversion.

  • Marketing mix and budget

    Estimate each channel's contribution and simulate budget allocation scenarios, with model uncertainty in view.

  • Predictive maintenance (where applicable)

    Anticipate failures from sensor data or logs, provided there is enough failure history.

  • Hydrological or consumption time series

    Short-term forecasting of physical variables with lags and comparison baselines.

Who it is for

  • Companies with a history of sales, orders, tickets or campaigns that want to anticipate demand or prioritise actions.
  • Marketing teams investing across several channels that want to understand what each contributes.
  • Businesses with equipment or facilities where anticipating failures or load peaks has value.

Realistic benefits

  • Decisions supported by figures and their uncertainty, not just intuition.
  • A clear reference point: you will know how much the model improves on the simplest option.
  • Knowledge of which variables really matter and which do not.
  • A retrainable system instead of a one-off analysis that expires.

Limits and honesty

  • There is no guarantee of accuracy: it depends on the data, how stable the phenomenon is and the horizon.
  • Predictive maintenance is only viable if there is enough history of failures or labelled anomalous behaviour.
  • A statistical model describes relationships; it does not prove causality without a suitable design.

Frequently asked questions

How much data do I need for a predictive model?

It depends on the problem. For time-series forecasting you want several full cycles (for example, at least a couple of years if there is yearly seasonality); for classification, enough examples of each class. We assess this in the initial analysis and, if it is not enough, I propose simpler alternatives.

How accurate will the model be?

I cannot give a figure before seeing the data. I can commit to measuring it honestly, with temporal validation and against a simple baseline, and to telling you if the result does not justify using the model.

Do you use machine learning or classical statistics?

Whatever fits the problem. Often a statistical model or a simple method with good features beats a complex one, and it is easier to explain and maintain.

Can you integrate the results into our BI or reports?

Yes. Results can be written to your data warehouse or delivered in a format consumed by Power BI, Looker or spreadsheets.

Can predictive maintenance be done with my equipment?

Only if sensor data or logs with enough failure history exist. If not, it is better to start by recording data properly and, meanwhile, use simple rules and alerts.

Related evidence: related projects

  • Machine learningPersonal project

    River level forecasting

    A hydrological forecasting project based on historical observations, lagged variables, baseline models and rigorous evaluation.

    • Python
    • Time series
    • Predictive modelling
    PrototypeView case

  • AnalyticsPersonal project

    Marketing Mix Modeling with PyMC

    An analytical project exploring marketing attribution, budget allocation and response modelling with Bayesian statistics in PyMC.

    • Python
    • PyMC
    • Statistical modelling
    PrototypeView case

Related services

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Let's talk about your case

A first conversation is enough to know whether it makes sense and where to start.

Request a proposalrubensolano.ia@gmail.com